For the task of experimental data estimation, the CNN model attempts to output a velocity field from artificial particle image as is detailed in Morimoto et al. [1]}.
As stated in section REF , we use an autoencoder (AE)-like CNN which comprises of 14 convolutional layers and 6 pooling/up-sampling layers.
In this case, it is able to estimate the role of each layer by extracting the output value of each hidden layer.
For the upper-stream layers, e.g., the 1st and 4th convolutional layers, the region around the square cylinder has larger values.
On the other hand, it is obvious that the downstream layers, e.g., the 12th and 13th layers, have relatively large intensities on the region with velocity fluctuation.
It implies that the upper-stream layers are responsible for recognizing the alignment of bluff body while the downstream layers attempts to output the velocity fluctuations.
